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b
e
an
y
t
y
p
e
o
f
f
iles
l
ik
e
a
t
ex
t,
an
i
m
a
g
e
o
r
an
au
d
io
f
ile
.
I
t
is
a
p
r
o
tectio
n
t
h
at
p
r
ev
en
t
s
s
u
s
p
icio
n
s
o
f
a
co
n
f
id
e
n
tial
m
e
s
s
a
g
e
p
r
esen
c
e.
On
l
y
s
e
n
d
er
an
d
r
ec
eiv
er
h
a
v
e
a
p
iece
o
f
k
n
o
wled
g
e
ab
o
u
t
th
e
e
m
b
ed
d
ed
m
e
s
s
a
g
e
[
2
]
,
[
6
]
.
A
u
d
io
f
iles
ar
e
co
n
s
id
er
ed
th
e
b
est
m
ed
ia
to
b
e
u
s
ed
as
a
co
v
er
b
ec
au
s
e
o
f
th
eir
s
e
n
s
i
tiv
it
y
,
c
ap
ac
it
y
,
an
d
w
id
esp
r
ea
d
av
ail
ab
ilit
y
[
6
]
.
A
u
d
io
s
teg
a
n
o
g
r
ap
h
y
tech
n
iq
u
es
m
a
y
b
e
ca
teg
o
r
ized
in
to
th
r
ee
t
y
p
e
s
.
T
h
e
f
ir
s
t
ca
teg
o
r
y
r
ef
e
r
s
to
tim
e
-
d
o
m
ai
n
tech
n
iq
u
es,
w
h
ich
h
id
e
m
e
s
s
a
g
es
in
th
e
least
s
i
g
n
i
f
ican
t
b
it
(
L
SB
)
o
f
ea
c
h
a
u
d
io
f
ile
s
a
m
p
le
th
a
t
co
n
tai
n
s
t
h
e
s
ec
r
et
m
e
s
s
a
g
e
b
in
ar
y
b
it
s
e
q
u
en
ce
s
[
7
]
.
A
lt
h
o
u
g
h
th
i
s
au
d
io
s
teg
a
n
o
g
r
ap
h
y
tec
h
n
iq
u
e
ca
n
h
id
e
lar
g
e
a
m
o
u
n
ts
o
f
d
ata,
th
ese
d
ata
m
a
y
b
e
s
i
m
p
l
y
d
etec
ted
b
ec
au
s
e
o
f
c
h
an
n
el
n
o
is
e
[
8
]
.
T
h
e
s
ec
o
n
d
ca
teg
o
r
y
is
tr
an
s
f
o
r
m
d
o
m
ai
n
tech
n
iq
u
e
s
th
at
u
s
ed
h
id
in
g
ef
f
ec
t
s
o
f
th
e
h
u
m
an
a
u
d
ito
r
y
s
y
s
te
m
b
y
m
a
k
in
g
t
h
e
lo
w
f
r
eq
u
en
c
ies
n
ea
r
t
h
e
h
i
g
h
f
r
eq
u
en
cie
s
in
a
u
d
ib
le
[
9
]
.
T
h
e
th
ir
d
ca
teg
o
r
y
is
a
w
a
v
elet
d
o
m
ai
n
m
et
h
o
d
th
at
u
s
es
d
is
cr
ete
w
av
ele
t
tr
an
s
f
o
r
m
(
D
W
T
)
to
h
id
e
th
e
m
e
s
s
a
g
es
in
th
e
L
SB
s
o
f
w
a
v
elet
co
ef
f
ic
ie
n
ts
,
to
e
n
h
an
ce
th
e
i
m
p
er
ce
p
tib
ilit
y
o
f
t
h
e
m
es
s
a
g
e;
d
ata
s
h
o
u
ld
b
e
h
id
d
en
in
h
ea
r
in
g
i
n
teg
er
w
a
v
elet
co
ef
f
icie
n
ts
.
Mo
r
eo
v
er
,
h
id
in
g
d
ata
in
s
ile
n
t
p
ar
ts
o
f
th
e
au
d
io
s
ig
n
al
m
u
s
t
b
e
av
o
id
ed
.
Hid
in
g
d
ata
in
th
e
w
a
v
elet
d
o
m
ai
n
p
r
o
d
u
ce
d
a
h
ig
h
e
m
b
ed
d
in
g
r
at
e,
b
u
t
t
h
e
d
ata
ex
tr
ac
ted
b
y
t
h
e
r
ec
eiv
er
m
a
y
co
n
ta
in
er
r
o
r
s
[
8
]
,
[
1
0
]
.
T
h
er
e
ar
e
s
o
m
e
class
ic
tec
h
n
iq
u
es
f
o
r
au
d
io
s
teg
an
o
g
r
ap
h
y
lik
e
ec
h
o
h
id
i
n
g
tech
n
iq
u
e,
p
h
ase
co
d
in
g
tec
h
n
iq
u
e,
an
d
s
p
r
ea
d
s
p
ec
tr
u
m
tec
h
n
iq
u
e.
No
ticin
g
th
at
th
e
s
e
tech
n
iq
u
e
s
in
d
u
ce
d
n
o
n
o
is
e
in
t
h
e
co
v
er
au
d
io
f
il
e
an
d
th
at’
s
w
h
y
it
co
n
s
id
er
ed
m
o
r
e
r
o
b
u
s
t f
o
r
St
eg
an
o
g
r
ap
h
y
ac
h
ie
v
e
m
en
t [
6
]
.
T
h
e
m
ai
n
id
ea
o
f
co
n
v
er
ti
n
g
a
n
i
m
ag
e
i
n
to
s
o
u
n
d
an
d
v
ice
v
er
s
a
r
ev
o
lv
e
s
ar
o
u
n
d
th
e
n
ee
d
to
u
n
d
er
s
tan
d
th
e
im
ag
e
f
r
o
m
an
o
t
h
er
p
er
s
p
ec
tiv
e.
T
h
is
h
elp
s
p
e
o
p
le
w
it
h
s
e
n
s
o
r
y
d
is
ab
ilit
ie
s
to
p
er
ce
iv
e
th
e
e
n
v
ir
o
n
m
en
t a
n
d
co
llect
in
f
o
r
m
atio
n
ab
o
u
t th
eir
s
u
r
r
o
u
n
d
in
g
s
[
1
1
]
.
I
n
th
e
f
r
eq
u
en
c
y
d
o
m
ai
n
,
t
h
e
co
v
er
f
i
le
is
tr
an
s
f
o
r
m
ed
to
th
e
f
r
eq
u
e
n
c
y
d
o
m
ai
n
s
o
f
f
a
s
t
Fo
u
r
ier
tr
an
s
f
o
r
m
(
FF
T
)
,
d
is
cr
ete
c
o
s
in
e
tr
an
s
f
o
r
m
(
DC
T
)
,
o
r
DW
T
to
ac
q
u
ir
e
th
e
tr
an
s
f
o
r
m
ed
co
ef
f
icie
n
t
s
.
T
h
ese
co
ef
f
icie
n
t
s
ar
e
u
s
ed
f
o
r
h
id
in
g
s
ec
r
et
m
ess
a
g
e
s
.
T
o
r
etr
i
ev
e
th
e
s
ec
r
et
m
e
s
s
a
g
e,
an
i
n
v
er
s
e
tr
an
s
f
o
r
m
is
ap
p
lied
to
th
e
co
v
er
co
ef
f
icie
n
ts
to
g
ai
n
h
id
d
en
d
ata.
T
h
er
e
a
r
e
m
a
n
y
d
if
f
er
en
ce
s
b
et
w
ee
n
t
i
m
e
a
n
d
f
r
eq
u
e
n
c
y
d
o
m
ai
n
tech
n
iq
u
e
s
,
f
o
r
ex
a
m
p
le,
th
e
tim
e
d
o
m
ai
n
is
m
o
r
e
ef
f
ec
tiv
e
w
it
h
attac
k
s
th
a
n
th
e
latter
b
ec
au
s
e
th
e
s
a
m
p
le
v
al
u
es
ar
e
m
o
d
i
f
ied
[
1
2
]
.
Fu
r
th
er
m
o
r
e,
th
e
e
n
co
d
in
g
o
p
er
atio
n
co
u
ld
b
e
u
til
iz
ed
to
ch
an
g
e
th
e
ap
p
ea
r
an
ce
o
f
d
ata
f
iles
f
r
o
m
th
e
i
m
ag
e
to
a
u
d
io
,
au
d
io
to
tex
t,
o
r
v
ice
v
er
s
a
to
cr
ea
te
a
n
e
w
s
h
ap
e
th
at
i
s
u
n
r
ec
o
g
n
izab
le
an
d
m
o
r
e
s
ec
u
r
ed
.
So
m
e
o
f
th
ese
o
p
er
atio
n
s
in
cl
u
d
e
au
d
io
-
to
-
i
m
a
g
e
tr
an
s
f
o
r
m
m
eth
o
d
s
b
as
ed
o
n
th
e
DW
T
an
d
th
e
d
i
s
cr
et
e
f
o
u
r
ier
tr
an
s
f
o
r
m
(
DFT
)
o
f
an
au
d
io
s
ig
n
al
,
i
n
v
er
s
e
f
as
t
f
o
u
r
ier
tr
a
n
s
f
o
r
m
(
I
FF
T
)
,
an
d
in
v
er
s
e
d
is
cr
ete
f
o
u
r
ier
tr
an
s
f
o
r
m
(
I
DFT
)
,
w
h
ic
h
ar
e
u
s
ed
to
ca
lcu
late
th
e
o
r
ig
i
n
al
a
u
d
io
s
ig
n
al
[
1
3
]
.
T
h
ese
m
et
h
o
d
s
ar
e
m
o
s
tl
y
u
s
ed
w
it
h
t
h
e
ap
p
lic
atio
n
o
f
v
is
io
n
t
h
at
aid
s
tech
n
o
lo
g
y
f
o
r
th
e
b
li
n
d
,
f
o
r
i
m
ag
e
r
ec
o
g
n
itio
n
b
ased
o
n
its
s
o
u
n
d
p
atter
n
s
o
r
th
e
cr
ea
tio
n
o
f
an
i
m
a
g
e
o
r
au
d
io
s
ig
n
at
u
r
e
[
1
3
]
,
[
1
4
]
.
T
h
is
w
o
r
k
p
r
o
p
o
s
ed
a
n
e
w
m
eth
o
d
to
tr
an
s
f
o
r
m
t
h
e
au
d
io
i
n
to
an
i
m
a
g
e
f
i
le
w
it
h
an
r
ed
–
g
r
ee
n
–
b
lu
e
(
R
GB
)
f
o
r
m
at
i
n
t
h
e
ti
m
e
d
o
m
ain
.
S
u
b
s
eq
u
e
n
tl
y
,
th
e
i
m
a
g
e
is
h
id
d
en
w
ith
in
a
n
au
d
io
f
i
le
u
s
in
g
t
w
o
tech
n
iq
u
e
s
,
n
a
m
e
l
y
,
t
h
e
L
SB
tec
h
n
iq
u
e
i
n
th
e
ti
m
e
d
o
m
ai
n
,
an
d
t
h
e
s
u
b
s
t
itu
tio
n
tech
n
iq
u
e
in
th
e
w
a
v
el
et
d
o
m
ai
n
.
A
l
s
o
,
to
s
h
o
w
th
e
e
f
f
ic
ien
c
y
o
f
th
e
p
r
o
p
o
s
ed
s
y
s
te
m
in
ter
m
s
o
f
s
e
cu
r
in
g
d
ata,
a
s
tatis
tical
an
al
y
s
i
s
is
u
s
ed
in
t
h
i
s
p
ap
er
f
o
r
test
in
g
th
e
tr
an
s
f
o
r
m
atio
n
m
et
h
o
d
an
d
co
m
p
ar
is
o
n
s
b
et
w
ee
n
h
id
in
g
u
s
in
g
L
S
B
an
d
w
a
v
elet.
T
h
is
w
o
r
k
co
n
tr
ib
u
te
s
to
f
i
n
d
in
g
a
n
in
e
x
p
en
s
iv
e
w
a
y
a
n
d
s
i
m
p
le
t
o
co
n
v
er
t
d
ata
f
r
o
m
v
o
ice
to
i
m
ag
e
w
it
h
t
h
e
ai
m
o
f
p
r
eser
v
in
g
it
an
d
k
ee
p
in
g
it
s
e
cu
r
el
y
w
ith
o
u
t
co
n
s
u
m
in
g
t
h
e
n
et
w
o
r
k
o
r
co
m
p
u
ter
r
eso
u
r
ce
s
in
s
av
i
n
g
an
d
tr
an
s
m
is
s
io
n
.
T
h
at
i
s
,
w
h
ate
v
e
r
th
e
s
ize
o
f
t
h
e
r
esu
lti
n
g
i
m
a
g
e
(
w
h
ic
h
w
a
s
m
o
s
tl
y
les
s
t
h
a
n
t
h
e
o
r
ig
i
n
al
s
ize)
,
it
w
i
ll
b
e
h
id
d
en
in
s
id
e
a
co
v
er
f
ile
an
d
s
en
t
to
t
h
e
r
ec
eiv
er
w
h
er
e
th
e
s
ize
o
f
t
h
e
co
v
er
f
i
l
e
w
ill
b
e
k
ep
t.
Fo
r
th
is
,
t
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
g
u
a
r
an
teed
th
e
i
n
teg
r
it
y
a
n
d
s
ec
u
r
it
y
o
f
d
ata
w
it
h
f
e
w
er
ef
f
o
r
ts
a
n
d
co
m
p
u
tatio
n
s
.
T
h
e
p
ap
er
is
s
tr
u
ct
u
r
ed
as
f
o
l
lo
w
s
;
s
ec
tio
n
1
in
cl
u
d
es
an
o
v
er
v
ie
w
a
n
d
d
ef
i
n
itio
n
s
f
o
r
th
e
r
esear
ch
s
u
b
j
ec
t,
s
ec
tio
n
2
d
is
cu
s
s
es
a
r
ev
ie
w
o
f
p
r
ev
io
u
s
s
t
u
d
ies
o
n
tr
an
s
f
o
r
m
atio
n
an
d
s
teg
a
n
o
g
r
a
p
h
y
.
T
h
e
p
r
o
p
o
s
ed
sy
s
te
m
s
tr
u
ct
u
r
e
is
d
escr
ib
ed
in
s
ec
tio
n
3
.
Sectio
n
4
p
r
esen
ts
th
e
ex
p
er
i
m
en
tal
r
esu
l
ts
an
d
an
al
y
s
i
s
.
Fin
a
ll
y
,
th
e
co
n
cl
u
s
io
n
s
a
n
d
f
u
tu
r
e
w
o
r
k
s
ar
e
s
h
o
w
n
i
n
s
ec
tio
n
5
.
2.
RE
L
AT
E
D
WO
RK
S
Sev
er
al
s
t
u
d
ies
h
av
e
b
ee
n
s
u
b
m
itted
to
i
m
p
le
m
e
n
t
a
h
i
g
h
lev
el
o
f
s
ec
u
r
it
y
to
p
r
o
tect
d
if
f
e
r
en
t
k
i
n
d
s
o
f
d
ata.
Sev
er
al
s
y
s
te
m
s
,
i
n
c
lu
d
in
g
th
o
s
e
t
h
at
r
el
y
o
n
en
cr
y
p
tio
n
b
y
m
a
k
i
n
g
d
ata
in
c
o
m
p
r
e
h
en
s
ib
le,
th
e
tr
an
s
f
o
r
m
atio
n
to
ch
an
g
e
d
ata
f
o
r
m
s
an
d
h
id
e
in
f
o
r
m
a
tio
n
co
n
tin
u
e
in
d
ev
elo
p
in
g
.
I
n
th
is
w
o
r
k
,
o
n
l
y
tr
an
s
f
o
r
m
atio
n
an
d
s
te
g
a
n
o
g
r
a
p
h
y
m
eth
o
d
s
ar
e
m
er
g
ed
to
s
ec
u
r
e
d
ata
.
2
.
1
.
T
ra
ns
f
o
r
m
a
t
io
n studies
1
P
en
g
et
al
.
[
1
5
]
p
r
o
p
o
s
ed
a
m
eth
o
d
th
at
tr
an
s
f
o
r
m
ed
a
d
i
g
it
al
au
d
io
s
i
g
n
a
l
b
y
co
n
v
er
ti
n
g
it
in
to
a
n
i
m
a
g
e
u
s
in
g
v
ir
t
u
al
o
p
tical
p
ar
a
m
eter
s
.
T
h
eir
m
e
th
o
d
d
ep
en
d
ed
o
n
w
a
v
e
p
o
s
itio
n
an
d
len
g
th
,
as
w
ell
t
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
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tio
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(
DFD)
.
T
h
e
b
asic
id
ea
w
as
co
m
e
f
r
o
m
a
s
p
ac
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Fre
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tr
an
s
f
o
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m
o
p
tical
p
r
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r
(
S
VFT
OP
)
w
h
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t
h
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i
n
f
o
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m
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s
h
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t
is
a
n
e
n
co
d
ed
s
o
u
n
d
m
ap
.
T
h
e
1
D
au
d
io
w
a
v
w
as
en
co
d
ed
as
a
2
D
m
atr
ix
as
a
2
D
s
o
u
n
d
m
ap
(
2
SM)
th
at
w
o
u
ld
b
e
s
to
r
ed
as
a
n
in
f
o
r
m
atio
n
s
h
ee
t
r
ep
r
esen
ted
an
o
r
d
in
ar
y
i
m
a
g
e
[
1
5
]
.
Kh
an
et
a
l
.
[
1
4
]
p
r
o
p
o
s
ed
an
al
g
o
r
ith
m
to
r
ec
o
g
n
i
ze
i
m
a
g
es
b
ased
o
n
th
eir
g
e
n
er
ated
s
o
u
n
d
p
atter
n
s
b
lin
d
p
eo
p
le
w
h
o
ar
e
s
u
f
f
er
i
n
g
f
r
o
m
v
is
u
al
p
er
ce
p
tio
n
lack
i
n
g
.
T
h
e
al
g
o
r
ith
m
g
en
er
ated
a
n
au
d
io
s
ig
n
at
u
r
e
f
r
o
m
a
n
i
m
a
g
e
w
it
h
t
w
o
p
h
ase
s
.
I
n
th
e
f
ir
s
t p
h
a
s
e,
ed
g
e
d
etec
t
io
n
is
p
r
o
ce
s
s
ed
i
n
all
d
ir
e
ctio
n
s
(
h
o
r
izo
n
tal,
v
er
ti
ca
l,
an
d
d
iag
o
n
al)
u
s
i
n
g
t
h
e
S
o
b
al
o
p
er
ato
r
.
T
h
en
,
t
h
e
le
n
g
t
h
a
n
d
o
r
ien
tatio
n
o
f
ea
ch
ed
g
e
ar
e
d
eter
m
in
ed
.
I
n
th
e
s
ec
o
n
d
p
h
ase,
th
r
ee
a
u
d
ib
le
f
r
eq
u
e
n
cies
ar
e
g
e
n
er
ated
o
n
ea
ch
ed
g
e
d
ir
ec
tio
n
(
n
a
m
el
y
,
h
o
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izo
n
tal
,
v
er
tical,
an
d
d
iag
o
n
al)
,
th
en
p
la
y
ed
as
a
s
o
u
n
d
[
1
4
]
.
Ya
-
Na
n
u
s
ed
ed
g
e
d
etec
tio
n
w
it
h
ca
n
n
y
o
p
er
ato
r
an
d
i
m
a
g
e
s
e
g
m
en
tatio
n
to
p
r
o
ce
s
s
i
m
ag
e
s
,
t
h
e
au
th
o
r
t
h
e
n
m
ap
p
ed
th
e
i
m
a
g
e
to
v
o
ice
p
atter
n
s
.
T
h
is
co
u
ld
b
e
u
s
ed
i
n
th
e
v
OI
C
e
s
y
s
te
m
b
y
h
elp
in
g
b
li
n
d
s
in
o
b
tai
n
i
n
g
s
o
m
e
i
n
f
o
r
m
atio
n
b
y
tr
ain
i
n
g
to
h
ea
r
th
e
s
e
p
atter
n
s
o
f
s
o
u
n
d
to
r
ec
o
g
n
ize
i
m
a
g
es
[
1
6
]
.
Z
h
an
g
et
a
l
.
[
1
3
]
p
r
o
p
o
s
ed
a
n
o
v
el
i
m
a
g
e
-
s
o
u
n
d
co
n
v
er
s
io
n
m
et
h
o
d
th
at
r
ed
u
ce
d
th
e
co
m
p
le
x
it
y
o
f
i
m
a
g
e
-
s
o
u
n
d
co
n
v
er
s
io
n
c
o
m
p
u
tatio
n
.
T
h
eir
m
et
h
o
d
tak
es
DFT
f
o
r
ea
ch
co
lu
m
n
o
f
an
i
m
a
g
e
as
an
au
d
io
s
ig
n
a
l
an
d
I
FF
T
is
u
s
ed
to
r
etr
iev
e
th
e
au
d
io
s
ig
n
al.
T
h
is
m
eth
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d
w
a
s
ef
f
ec
tiv
e
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d
s
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cc
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s
s
f
u
l
in
r
ea
l
-
ti
m
e
p
er
f
o
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m
an
ce
as
a
p
ar
t
o
f
th
e
v
OI
C
e
tech
n
o
lo
g
y
f
o
r
th
e
b
lin
d
.
2
.
2
.
Ste
g
a
no
g
r
a
ph
y
s
t
ud
ies
T
h
e
p
r
in
cip
le
o
f
s
te
g
a
n
o
g
r
a
p
h
y
ap
p
ea
r
ed
an
d
co
n
ti
n
u
ed
to
b
e
d
ev
elo
p
ed
as
a
m
ec
h
an
i
s
m
f
o
r
p
r
o
tectin
g
t
h
e
i
n
f
o
r
m
atio
n
o
f
an
y
t
y
p
e
o
f
f
ile
s
.
A
u
d
io
s
teg
a
n
o
g
r
ap
h
y
i
s
u
s
ed
to
h
id
e
au
d
i
o
s
ig
n
als.
T
h
e
b
asic
r
eq
u
ir
e
m
en
ts
o
f
an
a
u
d
io
s
teg
an
o
g
r
ap
h
y
s
y
s
te
m
ar
e
th
e
i
m
p
er
ce
p
tib
ilit
y
o
f
t
h
e
s
ec
r
et
m
es
s
ag
e,
t
h
e
h
id
i
n
g
ca
p
ac
it
y
s
h
o
u
ld
b
e
m
ax
i
m
ize
d
,
o
p
tio
n
all
y
,
th
e
h
id
d
en
d
ata
p
r
ef
er
r
ed
to
b
e
en
cr
y
p
ted
,
an
d
eith
er
o
r
b
o
th
th
e
m
es
s
ag
e
an
d
th
e
co
v
er
f
ile
m
u
s
t
b
e
an
au
d
io
s
ig
n
a
l
[
1
4
]
.
S
o
m
e
r
esear
ch
er
s
h
av
e
co
n
s
id
er
ed
th
e
tim
e
d
o
m
ai
n
o
f
th
e
au
d
io
f
ile
d
ata
as
a
g
o
o
d
c
h
o
ice
f
o
r
h
id
in
g
s
ec
r
et
m
es
s
ag
e
s
.
T
h
u
s
,
th
e
m
o
s
t
co
m
m
o
n
m
et
h
o
d
w
a
s
L
S
B
th
at
u
s
ed
b
y
K.
Go
p
alan
w
h
o
u
s
ed
L
SB
f
o
r
e
m
b
ed
d
in
g
a
n
au
d
io
m
e
s
s
a
g
e
in
a
co
v
er
u
tter
an
ce
f
ile.
T
h
e
s
ec
r
et
m
es
s
ag
e
w
a
s
co
m
p
r
es
s
ed
an
d
h
id
d
en
i
n
th
e
s
a
m
p
le
s
o
f
t
h
e
c
o
v
er
u
tter
an
ce
b
y
alte
r
in
g
o
n
e
b
it
in
ea
ch
s
a
m
p
l
e
ac
co
r
d
in
g
to
t
h
e
d
ata
b
it
s
w
it
h
a
k
e
y
,
w
h
ic
h
w
a
s
al
s
o
u
s
ed
to
r
etr
iev
e
t
h
e
h
id
d
en
b
it
s
at
th
e
r
ec
eiv
er
s
id
e
[
1
7
]
.
San
to
s
a
an
d
B
ao
[
1
8
]
p
r
o
p
o
s
ed
an
au
d
io
s
teg
an
o
g
r
ap
h
y
ap
p
r
o
ac
h
b
ased
o
n
DW
T
f
o
r
au
d
io
-
to
-
i
m
ag
e
co
n
v
er
s
io
n
.
I
n
t
h
eir
ap
p
r
o
ac
h
,
th
e
h
o
s
t
a
u
d
io
s
i
g
n
al
is
tr
an
s
f
o
r
m
ed
i
n
to
an
i
m
ag
e
a
n
d
th
en
h
id
d
en
in
an
i
m
a
g
e,
w
h
ic
h
is
t
h
en
tr
a
n
s
f
o
r
m
ed
b
ac
k
in
to
an
a
u
d
io
s
ig
n
al
[
1
8
]
.
I
n
2
0
1
5
,
Sin
h
a
et
a
l
.
[
1
9
]
p
r
o
p
o
s
ed
th
e
L
SB
m
et
h
o
d
f
o
r
co
n
ce
alin
g
en
cr
y
p
ted
tex
t
m
es
s
ag
e
s
.
First,
t
h
e
tex
t
m
e
s
s
a
g
e
w
as
e
n
cr
y
p
ted
u
s
i
n
g
a
Vi
g
e
n
èr
e
cip
h
er
alg
o
r
it
h
m
.
Seco
n
d
,
th
e
tex
t
m
es
s
ag
e
w
as
e
m
b
ed
d
ed
in
to
a
co
v
er
o
f
a
n
a
u
d
io
f
ile
u
s
in
g
t
h
e
L
SB
m
et
h
o
d
.
T
o
in
cr
ea
s
e
th
e
s
ec
u
r
it
y
le
v
el,
th
e
a
u
d
io
f
ile
w
as
m
an
ip
u
lated
w
it
h
B
lu
m
B
lu
m
S
h
u
b
p
s
e
u
d
o
r
an
d
o
m
n
u
m
b
er
g
e
n
er
a
to
r
to
tr
an
s
p
o
s
e
th
e
p
lace
s
o
f
t
h
e
s
a
m
p
le
s
[
1
9
]
.
Naser
i
et
a
l
.
[
2
0
]
p
r
esen
ted
a
n
e
w
s
ec
u
r
i
n
g
s
t
r
a
t
eg
y
b
as
e
d
o
n
w
a
t
e
r
m
a
r
k
s
f
o
r
q
u
an
tu
m
im
ag
es
.
T
h
ey
aim
e
d
t
o
h
i
d
e
d
a
t
a
u
s
in
g
th
e
L
S
B
an
d
th
e
m
o
s
t
s
ig
n
if
i
c
an
t
b
i
t
(
M
S
B
)
.
T
h
e
au
th
o
r
s
s
im
u
l
a
t
e
an
d
t
es
t
th
e
i
r
s
y
s
t
em
w
ith
p
e
ak
s
ig
n
a
l
-
to
-
n
o
is
e
r
a
ti
o
(
PS
NR
)
c
a
l
cu
l
a
ti
o
n
t
o
en
s
u
r
e
it
s
s
e
cu
r
ity
an
d
a
p
p
l
i
c
a
b
i
l
ity
c
o
m
p
a
r
e
d
w
ith
th
e
p
r
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o
u
s
s
tu
d
ie
s
f
o
u
n
d
i
n
th
at
p
e
r
i
o
d
[
2
0
]
.
I
n
th
e
f
r
eq
u
e
n
c
y
d
o
m
ai
n
,
Vi
s
w
a
n
at
h
an
[
2
1
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p
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o
d
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d
a
m
o
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el
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tex
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m
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en
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y
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o
m
ai
n
f
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at
(
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FT
)
.
T
h
e
m
es
s
ag
e
w
as
h
id
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en
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t
h
e
F
FT
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ig
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e
f
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ter
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in
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o
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h
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et
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o
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l
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p
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t
ta
m
p
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s
[
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1
]
.
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an
g
in
tr
o
d
u
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a
q
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m
r
ep
r
esen
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io
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ep
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led
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t
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le
-
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el
[
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2
]
.
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g
et
al
.
[
2
3
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p
r
esen
ted
a
s
teg
an
o
g
r
ap
h
y
–
s
teg
an
a
l
y
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is
s
y
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m
f
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au
d
i
o
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ig
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al
s
.
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h
is
s
y
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te
m
co
n
s
is
t
s
o
f
t
w
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ai
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te
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d
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s
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h
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e.
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elec
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ed
o
n
th
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ian
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lar
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er
s
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en
ce
.
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h
e
s
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p
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t
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at
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as
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ed
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d
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n
s
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es
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s
w
er
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f
o
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d
b
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ch
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e
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lc
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q
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a
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m
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s
ig
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al
f
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es.
T
h
is
q
u
an
t
u
m
m
o
d
el
w
as
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lated
an
d
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m
an
y
ti
m
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it
h
d
if
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er
e
n
t
w
av
e
f
iles
[
2
3
]
.
A
b
d
u
lr
az
za
q
et
a
l.
[
2
4
]
s
tu
d
ied
a
m
et
h
o
d
th
at
in
cl
u
d
ed
co
m
p
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n
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o
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il
e.
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ag
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as
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izat
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d
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s
c
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h
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a
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L
SB
la
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[
2
3
].
P
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u
s
m
et
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s
ar
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ch
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ac
ter
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3.
P
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1. Initially create a Matrix for empty image with dimension n×n×3,
where n=√M , M is the length of audio file
I_
RGB (
1:
n,
1:
n,
1:
3)
= zeros
2. For each sample Ai in audio file is manipulated with
these
formulas:
I_R=integer part (Ai*256)
I_G=integer part(fraction part (Ai) *256)
I_B=
{
255
,
≥
0
0
,
<
0
These variables represent a pixel in RGB image I_RGB.
3.
i=i+1
4.
Goto 2
5.
End
T
h
e
o
p
er
atio
n
o
f
m
u
ltip
licat
i
o
n
w
it
h
2
5
6
is
to
g
et
t
h
e
n
ea
r
est
i
n
teg
er
n
u
m
b
er
i
n
t
h
e
r
a
n
g
e
[
0
.
2
5
5
]
w
h
ic
h
i
s
d
esira
b
le
f
o
r
f
o
r
m
at
u
i
n
t8
t
h
at
r
ep
r
esen
ts
th
e
i
m
a
g
e
f
o
r
m
at.
ex1: A
= 0.95672
I_R=244
I_G=235
I_B= 1
i.e. the pixel is [244, 235,
1]
ex2: A=
-
0.52372
I_R=134
I_G=18
I_B= 0
i.e. th
e pixel is [134,
18,
0]
Af
ter
t
h
e
tr
an
s
f
o
r
m
a
tio
n
is
co
m
p
leted
,
th
e
r
es
u
lti
n
g
i
m
a
g
e
I
_
R
GB
is
s
to
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ed
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an
i
m
a
g
e
o
f
*
.
T
I
FF
f
o
r
m
at
to
s
till
r
etain
all
d
etails
o
f
th
e
o
r
ig
in
al
in
f
o
r
m
at
io
n
.
T
h
en
,
it
w
ill
b
e
h
id
d
en
in
an
au
d
io
co
v
er
f
ile.
On
t
h
e
r
ec
eiv
er
s
id
e,
t
h
e
tr
an
s
f
o
r
m
atio
n
p
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o
ce
s
s
is
r
ev
er
s
ed
b
y
u
s
in
g
a
d
iv
i
s
io
n
o
p
er
atio
n
to
r
etu
r
n
t
h
e
v
al
u
e
o
f
th
e
s
ec
r
et
s
a
m
p
le
as
f
o
llo
ws:
=
∗
(
(
_
+
(
_
/
256
)
)
/
256
)
(
1
)
w
h
er
e
is
eit
h
er
+1
o
r
−1
b
ased
o
n
th
e
v
al
u
e
o
f
I
_
B
,
th
at
is
,
i
f
it is
0
o
r
2
5
5
,
r
esp
ec
tiv
el
y
.
F
ig
u
r
e
1
s
h
o
w
s
th
e
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ac
co
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in
g
to
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ize
as s
h
o
w
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i
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ab
le
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N:
2502
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4752
S
ec
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a
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1783
T
ab
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1
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C
o
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a
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d
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i
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o
r
v
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i
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(
s)
2
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B
1
1
K
B
0
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0
1
5
0
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B
4
8
K
B
0
.
0
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9
4
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B
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1
2
4
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5
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3
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.
Cha
ra
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ics o
f
s
t
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a
no
g
ra
ph
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4
.
2
.
1
.
P
ea
k
d
ig
na
l t
o
n
o
is
e
r
a
t
io
(
P
SNR)
P
SNR
is
u
s
ed
to
ca
lcu
late
t
h
e
n
o
is
e
r
atio
f
o
r
th
e
co
v
er
f
i
le
b
ef
o
r
e
an
d
af
ter
h
id
i
n
g
to
ex
p
o
s
e
v
is
u
al
d
is
to
r
tio
n
af
ter
h
id
in
g
[
2
5
]
.
P
SNR
is
co
m
p
u
ted
w
it
h
th
e
(
2
)
:
PS
N
R
(
x
,
y
)
=
10
l
og
10
(
M
2
MS
E
)
(
2
)
w
h
er
e
is
th
e
b
ig
g
es
t
v
alu
e
o
f
th
e
s
am
p
le
s
.
m
ea
n
s
q
u
ar
e
er
r
o
r
(
MSE
)
i
s
th
e
b
et
w
ee
n
,
.
W
h
er
e
r
ep
r
esen
ts
th
e
o
r
i
g
in
a
l a
u
d
io
f
i
le
an
d
y
is
s
teg
o
f
ile
is
g
i
v
en
b
y
:
M
SE
=
∑
‖
x
(
i
)
−
y
(
j
)
‖
2
M
i
=
1
M
(
3
)
4
.
2
.
2
.
Str
uct
ura
l
s
i
m
ila
rit
y
ind
ex
m
et
ric
(
SS
I
M
)
SS
I
M
is
a
q
u
ality
m
etr
ic
u
s
ed
w
ith
im
ag
es.
I
t
is
b
etter
th
an
tr
ad
itio
n
al
m
ea
s
u
r
es,
s
u
ch
as
MSE
an
d
P
SNR
.
SS
I
M
co
n
s
id
er
s
im
ag
e
d
eg
r
ad
atio
n
as
a
ch
an
g
e
in
s
tr
u
ctu
r
al
in
f
o
r
m
atio
n
.
I
ts
w
o
r
k
is
b
ased
o
n
th
e
id
ea
th
at
s
p
atially
clo
s
e
im
ag
e
p
ix
els
h
av
e
s
tr
o
n
g
in
ter
d
ep
en
d
en
cies.
Fo
r
th
is
,
an
y
s
u
b
tle
ch
an
g
e
co
u
ld
b
e
m
ea
s
u
r
ed
u
s
in
g
th
is
m
etr
ic.
T
h
e
SS
I
M
is
ca
lcu
lated
b
etw
ee
n
th
e
en
cr
y
p
ted
s
ec
r
et
im
ag
e
an
d
th
e
r
etr
iev
ed
im
ag
e
at
th
e
r
ec
ip
ien
t sid
e
as g
iv
en
b
elo
w
:
SS
IM
=
(
2
x
y
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c
1
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(
2
σ
xy
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c
2
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(
σ
x
2
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σ
y
2
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c
2
)
(
x
2
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y
2
+
c
1
)
(
4
)
w
h
er
e
1
=
(
1
)
2
,
an
d
2
=
(
2
)
2
ar
e
b
o
th
co
n
s
tan
t
to
av
o
id
n
u
ll
d
o
m
i
n
ato
r
.
L
i
s
th
e
h
i
g
h
r
a
n
g
e
o
f
t
h
e
p
ix
el
v
al
u
es,
w
h
ich
i
s
2
5
5
.
k
1
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d
k
2
h
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d
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a
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lt
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s
o
f
0
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0
1
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d
0
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0
3
,
r
esp
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T
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tical
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d
s
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al
u
e
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w
h
ic
h
d
ec
r
e
ases
to
−1
as
s
o
u
n
d
s
c
h
a
n
g
e
[2
6
]
.
T
h
e
s
tatis
t
ical
a
n
al
y
s
is
r
esu
lt
s
s
h
o
w
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g
o
o
d
q
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alit
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f
o
r
s
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g
n
al
h
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n
g
as i
n
T
ab
le
2
.
T
ab
le
2
.
Statis
tical
an
al
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s
i
s
v
a
lu
es
f
o
r
P
SNR
,
MSE
,
SS
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M,
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d
ti
m
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n
s
u
m
p
tio
n
f
o
r
ap
p
ly
in
g
L
SB
an
d
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s
teg
o
m
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o
d
s
W
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t
h
L
S
B
W
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t
h
D
W
T
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i
z
e
o
f
me
ssag
e
P
S
N
R
M
S
E
S
S
I
M
T
i
me
(
s)
P
S
N
R
M
S
E
S
S
I
M
T
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me
(
s)
2
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2
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ized
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il
e
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f
s
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4
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6
9
MB.
T
h
e
ti
m
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n
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u
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f
f
er
en
t,
a
n
d
t
h
e
ti
m
e
o
f
DW
T
is
lo
w
er
t
h
an
th
a
t
o
f
L
SB
,
in
d
icati
n
g
th
at
t
h
e
f
o
r
m
er
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y
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te
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a
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m
ar
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th
a
n
t
h
e
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T
h
e
av
er
ag
e
P
SNR
v
al
u
es
f
o
r
b
o
th
m
et
h
o
d
s
w
er
e
ab
o
v
e
2
0
d
B
as
s
h
o
w
n
in
T
ab
le
2
.
T
h
is
lev
el
is
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ec
o
m
m
e
n
d
ed
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y
t
h
e
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ter
n
a
tio
n
al
f
ed
er
atio
n
o
f
th
e
p
h
o
n
o
g
r
ap
h
ic
i
n
d
u
s
tr
y
(
I
F
P
I
)
[
1
2
]
,
[2
7
]
.
T
h
e
s
ize
o
f
th
e
s
ec
r
et
f
ile
s
w
as
b
et
w
ee
n
(
2
6
KB
-
4
MB
)
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h
ic
h
w
er
e
h
id
d
en
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v
er
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ile
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4
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6
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d
all
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er
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th
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n
e
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m
p
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r
ate
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an
g
ed
b
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w
ee
n
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1
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0
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4
8
0
0
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)
.
Ho
w
e
v
er
,
it
i
s
v
er
y
c
lear
th
at
th
e
L
SB
m
e
th
o
d
s
co
r
ed
a
b
etter
r
an
g
e
(
8
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–
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d
B
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in
P
SNR
t
h
an
DW
T
,
w
h
i
ch
also
o
b
tai
n
ed
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l
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s
co
r
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(
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n
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e
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t
h
e
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o
v
er
f
ile)
.
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n
SS
I
M,
T
h
e
L
SB
m
e
th
o
d
h
as
a
b
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s
i
m
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n
d
ex
f
o
r
th
e
r
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ed
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m
ag
e
b
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au
s
e
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s
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r
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v
alu
e
f
o
r
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h
id
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ile
s
is
1
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i
v
e
n
i
n
T
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le
2
,
w
h
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h
i
n
d
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ca
tes its
ef
f
ec
ti
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e
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ess
in
m
a
in
t
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th
e
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n
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g
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s
m
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ata.
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T
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d
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h
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h
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tle
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er
e
n
ce
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
22
,
No
.
3
,
J
u
n
e
2
0
2
1
:
1
7
7
7
-
1
7
8
6
1784
Nev
er
th
e
less
,
t
h
e
u
s
e
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h
i
s
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d
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y
a
f
f
ec
t
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y
s
u
c
h
a
s
u
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tle
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g
e.
Fi
g
u
r
e
4
s
h
o
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a
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e
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a
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at
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ile
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g
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r
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4
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et
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d
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v
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d
io
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ter
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id
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n
g
w
i
th
L
SB
an
d
DW
T
5.
CO
NCLU
SI
O
N
A
n
e
w
a
n
d
s
ec
u
r
ed
tr
an
s
f
o
r
m
a
tio
n
m
eth
o
d
is
p
r
o
p
o
s
ed
in
t
h
i
s
w
o
r
k
.
T
h
is
tr
a
n
s
f
o
r
m
atio
n
is
ch
an
g
i
n
g
th
e
t
y
p
e
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a
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m
e
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g
e
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r
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m
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io
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to
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m
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g
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at
w
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e
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if
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t
h
at
k
ee
p
s
all
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ts
d
etail
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d
v
alu
e
s
.
T
h
e
in
tr
o
d
u
ce
d
m
eth
o
d
en
ab
les
r
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u
s
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e
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r
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m
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s
ize
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e
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ig
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au
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d
in
m
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r
it
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d
s
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p
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io
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g
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et
h
o
d
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w
er
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p
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o
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m
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a
m
el
y
;
L
SB
an
d
DW
T
.
T
h
ese
m
et
h
o
d
s
ac
h
ie
v
ed
g
o
o
d
s
co
r
es
f
o
r
P
SNR
,
MSE
,
an
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[1
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[5
]
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Yu
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lg
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tap
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io
m
e
tri
c
,
a
n
d
so
f
t
c
o
m
p
u
ti
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.
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h
a
s
p
u
b
l
ish
e
d
re
g
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lar
a
rti
c
les
f
o
r
m
o
re
th
a
n
4
0
IEE
E
In
tern
a
ti
o
n
a
l
C
o
n
f
e
re
n
c
e
s
a
n
d
Hig
h
-
q
u
a
li
ty
a
rti
c
les
in
S
CI
jo
u
r
n
a
ls,
a
n
d
h
e
h
o
l
d
s
3
in
tern
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ti
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l
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tern
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o
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re
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p
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h
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s
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lw
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se
r
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w
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f
o
r
se
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o
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n
d
h
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s
se
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e
d
a
s
th
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C
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a
ir/
P
C
m
e
m
b
e
r
f
o
r
m
o
re
th
a
n
2
5
i
n
tern
a
ti
o
n
a
l
c
o
n
f
e
re
n
c
e
s.
He
h
a
s
g
o
t
th
e
Be
st P
a
p
e
r
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a
rd
th
a
t
p
u
b
li
s
h
e
d
i
n
th
e
1
1
t
h
In
tern
a
ti
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
G
re
e
n
,
P
e
rv
a
siv
e
,
a
n
d
Clo
u
d
Co
m
p
u
ti
n
g
(G
P
C1
6
)
,
Xia
n
,
Ch
in
a
,
in
M
a
y
2
0
1
6
.
A
lso
,
h
e
p
a
rti
c
ip
a
ted
a
s
a
v
isit
in
g
sc
h
o
lar
p
ro
g
ra
m
m
e
f
o
r
in
tern
a
ti
o
n
a
l
re
se
a
rc
h
e
rs
to
Hu
a
z
h
o
n
g
Un
iv
e
rsity
o
f
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
a
n
d
S
h
e
n
z
h
e
n
In
sti
tu
te
in
2
0
1
8
a
n
d
2
0
1
9
.
M
u
sta
fa
A.
Al
S
ib
a
h
e
e
re
c
e
iv
e
d
th
e
B
h
d
.
d
e
g
re
e
f
ro
m
th
e
Hu
a
z
h
o
n
g
Un
iv
e
rsity
o
f
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
lo
g
y
,
W
u
h
a
n
,
C
h
i
n
a
,
in
2
0
1
8
.
He
is
c
u
rre
n
t
ly
a
Re
se
a
rc
h
e
r
w
it
h
th
e
S
h
e
n
z
h
e
n
In
stit
u
te,
H
u
a
z
h
o
n
g
Un
iv
e
rsit
y
o
f
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
lo
g
y
,
S
h
e
n
z
h
e
n
,
Ch
i
n
a
.
He
is
a
lso
a
L
e
c
tu
re
r
w
it
h
t
h
e
De
p
a
rtm
e
n
t
o
f
Co
m
m
u
n
ica
ti
o
n
En
g
in
e
e
rin
g
,
Ira
q
Un
iv
e
rsity
Co
ll
e
g
e
,
Ba
sra
h
,
Ira
q
.
His
re
se
a
rc
h
in
ter
e
sts
in
c
lu
d
e
c
o
m
p
u
ter
n
e
tw
o
rk
s
a
n
d
in
f
o
rm
a
ti
o
n
se
c
u
rit
y
,
c
o
m
p
u
ter
n
e
tw
o
rk
m
e
a
su
re
m
e
n
ts,
m
a
c
h
in
e
lea
rn
in
g
a
lg
o
rit
h
m
s
a
p
p
li
c
a
ti
o
n
s,
w
irele
ss
se
n
so
r
n
e
tw
o
rk
s
(
W
S
N),
so
f
t
wa
re
d
e
n
e
d
n
e
tw
o
rk
in
g
(S
DN
),
e
m
b
e
d
d
e
d
s
y
ste
m
s,
a
n
d
c
y
b
e
r
p
h
y
sic
a
l
s
y
ste
m
s (CP
S
)
.
M
o
h
a
m
m
e
d
A
b
d
u
lri
d
h
a
H
u
s
sa
in
re
c
e
iv
e
d
th
e
b
a
c
h
e
lo
r'
s
d
e
g
r
e
e
f
ro
m
th
e
De
p
a
rt
m
e
n
t
o
f
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m
p
u
ter
En
g
i
n
e
e
rin
g
,
C
o
ll
e
g
e
o
f
En
g
in
e
e
rin
g
,
Un
iv
e
rsity
o
f
Ba
sra
h
,
Ba
sra
h
,
I
ra
q
,
i
n
2
0
0
4
,
th
e
m
a
ste
r
'
s
d
e
g
r
e
e
in
c
o
m
p
u
ter
sc
ien
c
e
a
n
d
e
n
g
in
e
e
rin
g
f
ro
m
th
e
S
c
h
o
o
l
o
f
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
lo
g
y
,
G
u
ru
G
o
b
in
d
S
in
g
h
In
d
ra
p
ra
sth
a
Un
iv
e
rsity
,
De
lh
i,
In
d
ia,
i
n
2
0
0
9
,
a
n
d
t
h
e
P
h
.
D.
d
e
g
re
e
in
c
o
m
p
u
ter
e
n
g
in
e
e
rin
g
fro
m
th
e
De
p
a
rt
m
e
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
lo
g
y
,
Hu
a
z
h
o
n
g
U
n
iv
e
rsity
o
f
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
lo
g
y
,
Ch
in
a
,
in
2
0
1
7
.
He
is
c
u
rre
n
tl
y
a
L
e
c
tu
re
r
w
it
h
th
e
De
p
a
rtm
e
n
t
o
f
Co
m
p
u
te
r
S
c
ien
c
e
,
C
o
ll
e
g
e
o
f
Ed
u
c
a
ti
o
n
f
o
r
P
u
re
S
c
ie
n
c
e
,
Un
iv
e
rsity
o
f
Ba
sra
h
.
His
re
se
a
rc
h
i
n
tere
sts
in
c
l
u
d
e
n
e
tw
o
rk
s
e
c
u
rit
y
,
d
a
ta
s
e
c
u
rit
y
,
c
lo
u
d
se
c
u
rit
y
,
a
n
d
n
e
tw
o
rk
in
g
.
Za
id
Ala
a
H
u
ss
ie
n
re
c
e
i
v
e
d
th
e
B.
S
c
.
d
e
g
re
e
in
c
o
m
p
u
ter
e
n
g
in
e
e
rin
g
f
ro
m
th
e
Un
iv
e
rsit
y
o
f
Ba
sra
h
,
Ira
q
,
in
2
0
0
4
,
th
e
M
.
T
e
c
h
.
d
e
g
re
e
in
c
o
m
p
u
ter
sc
ien
c
e
a
n
d
e
n
g
in
e
e
rin
g
f
ro
m
G
u
ru
G
o
b
in
d
S
i
n
g
h
In
d
ra
p
ra
sth
a
Un
i
v
e
rsit
y
,
In
d
ia,
in
2
0
0
9
,
a
n
d
t
h
e
P
h
.
D.
d
e
g
re
e
in
c
o
m
p
u
ter
e
n
g
in
e
e
rin
g
f
ro
m
D
e
p
a
rt
m
e
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
lo
g
y
,
Hu
a
z
h
o
n
g
Un
iv
e
rsit
y
o
f
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
lo
g
y
,
Ch
in
a
,
in
2
0
1
7
.
He
is
c
u
rre
n
tl
y
w
o
rk
in
g
a
s
th
e
He
a
d
o
f
th
e
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
De
p
a
rt
m
e
n
t,
M
a
n
a
g
e
m
e
n
t
T
e
c
h
n
ica
l
Co
ll
e
g
e
,
S
o
u
th
e
r
n
T
e
c
h
n
ica
l
Un
iv
e
rsit
y
,
Ira
q
.
His
re
se
a
rc
h
in
t
e
re
sts
in
c
lu
d
e
c
lo
u
d
se
c
u
rit
y
,
se
a
rc
h
a
b
le
e
n
c
ry
p
ti
o
n
sy
ste
m
s,
a
u
th
e
n
ti
c
a
ti
o
n
a
n
d
i
n
teg
ra
ti
o
n
d
a
t
a
in
c
l
o
u
d
,
th
e
In
tern
e
t
o
f
T
h
in
g
s,
a
n
d
n
e
tw
o
rk
se
c
u
ri
t
y
.
He
is a Rev
ie
w
e
r
o
f
s
e
v
e
r
a
l
jo
u
rn
a
ls
a
n
d
i
n
tern
a
ti
o
n
a
l
c
o
n
f
e
re
n
c
e
s.
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